### Abstract

Integration of wind power with the grid has become an important problem. For integration, a producer needs to bid in a time-ahead market to deliver an amount of energy at a future point in time. Because wind speed and price are both uncertain, a producer needs to place bids on the basis of expected wind power yield and price. To this end, improving the accuracy of the prediction of wind speed has received much attention. However, the trade-off between expected profit and the prediction errors over a multi-period setting has been less studied. We fill this gap by quantifying trade-offs between profits and prediction errors. First, we obtain, under idealized conditions on the price and the yield processes, an optimal bid strategy as a closed-form expression. Next, we evaluate the profit-vs-prediction trade-off using this idealized bidding strategy on synthetic datasets which satisfy all the idealistic assumptions. We also consider two baselines - a naive strategy and an oracle strategy that has perfect knowledge over a limited horizon. Finally, we relax our assumptions and evaluate all strategies under real-world datasets. We identify and work around limitations of the idealized bidding strategy when the underlying assumptions are violated. On synthetic datasets, with no buffering and a (relative) prediction error of 25% , we find that our bidding approach performs significantly better than a naive approach and compares favourably (86%) to an oracle with a look-ahead of two time-slots and infinite buffer. On real-world datasets, with buffer equivalent to 20% of the maximum yield, our approach exceeds the naive approach by 25%, while remaining within 62% of a two-step look-ahead oracle that uses infinite buffering.

Original language | English (US) |
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Title of host publication | e-Energy 2014 - Proceedings of the 5th ACM International Conference on Future Energy Systems |

Publisher | Association for Computing Machinery |

Pages | 39-49 |

Number of pages | 11 |

ISBN (Print) | 9781450328197 |

DOIs | |

State | Published - Jan 1 2014 |

Event | 5th ACM International Conference on Future Energy Systems, e-Energy 2014 - Cambridge, United Kingdom Duration: Jun 11 2014 → Jun 13 2014 |

### Publication series

Name | e-Energy 2014 - Proceedings of the 5th ACM International Conference on Future Energy Systems |
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### Other

Other | 5th ACM International Conference on Future Energy Systems, e-Energy 2014 |
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Country | United Kingdom |

City | Cambridge |

Period | 6/11/14 → 6/13/14 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Energy Engineering and Power Technology
- Fuel Technology

### Cite this

*e-Energy 2014 - Proceedings of the 5th ACM International Conference on Future Energy Systems*(pp. 39-49). (e-Energy 2014 - Proceedings of the 5th ACM International Conference on Future Energy Systems). Association for Computing Machinery. https://doi.org/10.1145/2602044.2602056

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*e-Energy 2014 - Proceedings of the 5th ACM International Conference on Future Energy Systems.*e-Energy 2014 - Proceedings of the 5th ACM International Conference on Future Energy Systems, Association for Computing Machinery, pp. 39-49, 5th ACM International Conference on Future Energy Systems, e-Energy 2014, Cambridge, United Kingdom, 6/11/14. https://doi.org/10.1145/2602044.2602056

**Windy with a chance of profit - Bid strategy and analysis for wind integration.** / Kurandwad, Sagar; Subramanian, Chandrasekar; Ramakrishna P, Venkata; Vasan, Arunchandar; Sarangan, Venkatesh; Chellaboina, Vijaysekhar; Sivasubramaniam, Anand.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Windy with a chance of profit - Bid strategy and analysis for wind integration

AU - Kurandwad, Sagar

AU - Subramanian, Chandrasekar

AU - Ramakrishna P, Venkata

AU - Vasan, Arunchandar

AU - Sarangan, Venkatesh

AU - Chellaboina, Vijaysekhar

AU - Sivasubramaniam, Anand

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Integration of wind power with the grid has become an important problem. For integration, a producer needs to bid in a time-ahead market to deliver an amount of energy at a future point in time. Because wind speed and price are both uncertain, a producer needs to place bids on the basis of expected wind power yield and price. To this end, improving the accuracy of the prediction of wind speed has received much attention. However, the trade-off between expected profit and the prediction errors over a multi-period setting has been less studied. We fill this gap by quantifying trade-offs between profits and prediction errors. First, we obtain, under idealized conditions on the price and the yield processes, an optimal bid strategy as a closed-form expression. Next, we evaluate the profit-vs-prediction trade-off using this idealized bidding strategy on synthetic datasets which satisfy all the idealistic assumptions. We also consider two baselines - a naive strategy and an oracle strategy that has perfect knowledge over a limited horizon. Finally, we relax our assumptions and evaluate all strategies under real-world datasets. We identify and work around limitations of the idealized bidding strategy when the underlying assumptions are violated. On synthetic datasets, with no buffering and a (relative) prediction error of 25% , we find that our bidding approach performs significantly better than a naive approach and compares favourably (86%) to an oracle with a look-ahead of two time-slots and infinite buffer. On real-world datasets, with buffer equivalent to 20% of the maximum yield, our approach exceeds the naive approach by 25%, while remaining within 62% of a two-step look-ahead oracle that uses infinite buffering.

AB - Integration of wind power with the grid has become an important problem. For integration, a producer needs to bid in a time-ahead market to deliver an amount of energy at a future point in time. Because wind speed and price are both uncertain, a producer needs to place bids on the basis of expected wind power yield and price. To this end, improving the accuracy of the prediction of wind speed has received much attention. However, the trade-off between expected profit and the prediction errors over a multi-period setting has been less studied. We fill this gap by quantifying trade-offs between profits and prediction errors. First, we obtain, under idealized conditions on the price and the yield processes, an optimal bid strategy as a closed-form expression. Next, we evaluate the profit-vs-prediction trade-off using this idealized bidding strategy on synthetic datasets which satisfy all the idealistic assumptions. We also consider two baselines - a naive strategy and an oracle strategy that has perfect knowledge over a limited horizon. Finally, we relax our assumptions and evaluate all strategies under real-world datasets. We identify and work around limitations of the idealized bidding strategy when the underlying assumptions are violated. On synthetic datasets, with no buffering and a (relative) prediction error of 25% , we find that our bidding approach performs significantly better than a naive approach and compares favourably (86%) to an oracle with a look-ahead of two time-slots and infinite buffer. On real-world datasets, with buffer equivalent to 20% of the maximum yield, our approach exceeds the naive approach by 25%, while remaining within 62% of a two-step look-ahead oracle that uses infinite buffering.

UR - http://www.scopus.com/inward/record.url?scp=84907011952&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84907011952&partnerID=8YFLogxK

U2 - 10.1145/2602044.2602056

DO - 10.1145/2602044.2602056

M3 - Conference contribution

AN - SCOPUS:84907011952

SN - 9781450328197

T3 - e-Energy 2014 - Proceedings of the 5th ACM International Conference on Future Energy Systems

SP - 39

EP - 49

BT - e-Energy 2014 - Proceedings of the 5th ACM International Conference on Future Energy Systems

PB - Association for Computing Machinery

ER -